import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


lowerCamelCase__  =					logging.getLogger(__name__)




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    False







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   List[Any]							, lowercase_				:   Tuple							, lowercase_				:   Optional[int]							, lowercase_				:   List[str])						->	List[Any]:


   """simple docstring"""







   if not self.initialized:
     _UpperCamelCase           =    RagRetriever(
         lowercase_							, question_encoder_tokenizer=lowercase_							, generator_tokenizer=lowercase_							, index=lowercase_							, init_retrieval=lowercase_							, )
     _UpperCamelCase           =    True







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Optional[int]:


   """simple docstring"""







   self.retriever.index.init_index()







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   List[str]							, lowercase_				:   Tuple)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self.retriever._main_retrieve(lowercase_							, lowercase_)
   return doc_ids, retrieved_doc_embeds




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   Optional[int]							, lowercase_				:   Any							, lowercase_				:   Dict							, lowercase_				:   List[str]							, lowercase_				:   Dict							, lowercase_				:   List[Any]=None)						->	List[str]:


   """simple docstring"""







   if index is not None and index.is_initialized() and len(lowercase_) > 0:
     raise ValueError(
         "When using Ray for distributed fine-tuning, "
         "you'll need to provide the paths instead, "
         "as the dataset and the index are loaded "
         "separately. More info in examples/rag/use_own_knowledge_dataset.py ")
   super().__init__(
       lowercase_							, question_encoder_tokenizer=lowercase_							, generator_tokenizer=lowercase_							, index=lowercase_							, init_retrieval=lowercase_							, )
   _UpperCamelCase           =    retrieval_workers
   if len(self.retrieval_workers) > 0:
     ray.get(
         [
             worker.create_rag_retriever.remote(lowercase_							, lowercase_							, lowercase_							, lowercase_)
             for worker in self.retrieval_workers
         ])







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Optional[int]:


   """simple docstring"""







   logger.info("initializing retrieval")

   if len(self.retrieval_workers) > 0:
     ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
   else:
     # Non-distributed training. Load index into this same process.
     self.index.init_index()







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[Any]							, lowercase_				:   Any)						->	int:


   """simple docstring"""







   if len(self.retrieval_workers) > 0:
     # Select a random retrieval actor.
     _UpperCamelCase           =    self.retrieval_workers[random.randint(0							, len(self.retrieval_workers) - 1)]
     _UpperCamelCase      ,						_UpperCamelCase           =    ray.get(random_worker.retrieve.remote(lowercase_							, lowercase_))
   else:
     _UpperCamelCase      ,						_UpperCamelCase           =    self._main_retrieve(lowercase_							, lowercase_)
   return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase_)







 @classmethod
 def 		__UpperCAmelCase				(		cls				:   List[str]							, lowercase_				:   Any							, lowercase_				:   List[Any]=None							, **lowercase_				:   Tuple)						->	Any:


   """simple docstring"""







   return super(lowercase_							, cls).get_tokenizers(lowercase_							, lowercase_							, **lowercase_)







 @classmethod
 def 		__UpperCAmelCase				(		cls				:   Optional[int]							, lowercase_				:   Union[str, Any]							, lowercase_				:   int							, lowercase_				:   int=None							, **lowercase_				:   Optional[int])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.pop("config"							, lowercase_) or RagConfig.from_pretrained(lowercase_							, **lowercase_)
   _UpperCamelCase           =    RagTokenizer.from_pretrained(lowercase_							, config=lowercase_)
   _UpperCamelCase           =    rag_tokenizer.question_encoder
   _UpperCamelCase           =    rag_tokenizer.generator
   if indexed_dataset is not None:
     _UpperCamelCase           =    "custom"
     _UpperCamelCase           =    CustomHFIndex(config.retrieval_vector_size							, lowercase_)
   else:
     _UpperCamelCase           =    cls._build_index(lowercase_)
   return cls(
       lowercase_							, question_encoder_tokenizer=lowercase_							, generator_tokenizer=lowercase_							, retrieval_workers=lowercase_							, index=lowercase_							, )





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




